Automated Skin Cancer Diagnosis and Localization Using Deep Reinforcement Learning

Abstract

Skin cancer is a significant and life-threatening medical condition caused by abnormal cell growth in the skin. Early detection and accurate diagnosis are critical for effective treatment and improved patient outcomes. However, manual diagnosis of skin cancer can be time-consuming and prone to inter-observer variability, necessitating the development of automated and objective diagnostic approaches. This research purports to diagnose and localize skin cancer automatically utilizing a novel Deep Reinforcement Learning technique. The proposed approach leverages a modified version of the Asynchronous Advantage Actor Critic (A3C) algorithm, comprising multiple independent agents represented as Convolutional Neural Networks (CNNs). These agents interact with the environment (skin images) and perform segmentation actions based on policies that maximize expected rewards, promoting accurate segmentation and penalizing false negatives as well as false positives. The research employs three diverse skin cancer datasets, namely PH2, ISIC 2018, and ISIC 2017 in order to assess the proposed approach’s efficiency. Quantitative evaluation metrics, including Precision, Specificity, Accuracy, Jaccard Index (IoU), Sensitivity, Dice coefficient, and F1-score are used to assess segmentation accuracy. The proposed method is compared with advanced deep-learning algorithms commonly used for skin cancer segmentation. The results demonstrate the superiority of the proposed approach in accurately localizing cancerous regions under various challenging conditions, such as hair presence, image blurriness, and the presence of other objects or obstacles. The proposed approach outperforms existing methods in terms of segmentation accuracy (98.8%) in diverse skin image sources.